Trustworthy Intrusion Detection in E-Healthcare Systems - Akram, Faiza; Liu, Dongsheng; Zhao, Peibiao; Kryvinska, Natalia; Abbas, Sidra; Rizwan, Muhammad (Frontiers in Public Health, 2021)
In the Internet of things (IoT) based network systems (IoT-net), intrusion detection systems (IDS) plays a significant role to maintain patient health record (PHR) in e-Healthcare. IoT-net is a massive technology with security threats on the network layer, as it is considered the most common source for communication and data storage platforms. The security of data servers in all sectors (mainly healthcare) has become one of the most crucial challenges for researchers.} This paper proposes an approach for effective intrusion detection in the e-healthcare environment to maintain PHR in a safe IoT-net using an adaptive neuro-fuzzy inference system (ANFIS). In the proposed security model, the experiments present the security tool that helps to detect malicious network traffic. The practical implementation of the ANFIS model on MATLAB framework with testing and training results compares the accuracy rate from the previous research in security.